Money VS 0.30000000000000004

moneyphp: PHP implementation of Fowler's Money pattern

There's also https://github.com/moneyphp/money, which is great.

Or you can use MoneyPHP, which uses BCMath, and it takes care of all of your problems.

Instead, you want to use a Money object of some sort instead; e.g. https://github.com/moneyphp/money.

Yeah I usually use moneyphp for this

https://github.com/moneyphp/money to handle money

With that in place, let's talk about our test project for a minute. We are going to build a small library on top of Money, a popular PHP package facilitating the manipulation of monetary values and currencies.

In addition to the essential advice above to use Integers of the lowest denomination to store prices, you can also look into using a library to help with all the money math, conversions, comparisons etc https://github.com/moneyphp/money

Read the readme from moneyphp: https://github.com/moneyphp/money

https://0.30000000000000004.com/

Although it would be good to move in the direction of using a BigDecimal equivalent by default when ingesting unknown data.

There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/

Edit: This specific example even has its own website: https://0.30000000000000004.com/

In everyday math, we know adding 0.6 + 0.3 equals 0.9, right? But when we turn to computers it results in 0.8999999999999999. Surprisingly, this doesn’t just happen only in JavaScript; it’s the same in many programming languages like Python, Java, C too. Also, it’s not just about this specific calculation. There are many more decimal calculations showing similar not-quite-right answers.

Surprisingly common values like 0.1 don't have a precise representation in binary for most formats, including standard floating point number formats. See https://0.30000000000000004.com/ for more detail than you can shake a stick at.

Also if the local tax code states using 5 decimal places for intermediate values when you will introduce “errors” using formats that give greater precision as well as those that give less precision. Having work on mortgage and pension calculations I can state that the (very) small errors seen at individual steps because of this can balloon significantly through repeated calculations.

Furthmore, the name floating point gives away the other issue. Floating point numbers are accurate to a given number of significant figures not decimal places. For large numbers any decimal places you have in the result are at best an estimate, and as above any rounding errors at each stage can compound into a much larger error by the end of a calculation.

You'll love this https://0.30000000000000004.com/